This invention relates to temperature and pressure sensor fault detection for a heating, ventilation and air conditioning system.
Typically, a heating, ventilation and air conditioning (HVAC) system includes temperature and pressure sensors that are crucial to ensure proper operation of the system. A controller uses data from the sensors to adjust operation of the components such as an evaporator, compressor and condenser. If the sensors fail or are inaccurate, the HVAC system will not operate as intended. Further, if the sensors fail to provide accurate data, the components within the HVAC system may operate outside desirable parameters, potentially causing premature failure. For these reasons, it is desirable to monitor sensor accuracy and indicate a fault condition when sensor accuracy is outside a desired operating range.
Prior art sensor fault detection techniques include the use of redundant sensors to measure the same parameter within the system. The difference of data measured between the two redundant sensors is detected and the difference compared to a threshold value. If the threshold value is exceeded by the actual difference, then a fault is detected and corrective action is initiated.
Systems that use redundant sensors are cost-prohibitive. Further, redundant sensors double the programming requirements and controller requirements, increasing the overall complexity of the system.
Another known prior art system detects pressure sensor faults by subtracting evaporator pressure from condenser pressure. A faulty sensor is indicated if the difference between the pressure sensor at the evaporator and the pressure sensor at the condenser is zero or negative. Condenser pressure should always be higher than pressure within the evaporator and a deviation from this indicates a failure. Disadvantageously, this approach can only detect complete sensor failure. Sensor drift or bias where the sensor provides inaccurate information cannot be detected by such a system.
Another known prior art system uses sensor data to determine a linear relationship that represents a system of statistically related components. A sensor fault is detected by a detected deviation from that linear relationship. This data-driven approach requires a large amount of linearly related data and a statistical analysis that is complicated and computationally intensive. The large amounts of computation can lead to inaccuracies and increased costs.
Accordingly, it is desirable to develop a sensor fault detection method and system that works in real time, uses localized features as well as long-term trending features obtained from data to detect sensor defects, and adapt to current operating systems.